System and method for efficient service-instance oriented energy management in the internet of things

ABSTRACT

A system and a method for efficient service-instance oriented energy management in the IoT are provided. The method comprises: a predicting step for predicting target service instances to be serviced in a subsequent time period based on a service instance stance transition model; a selecting step for selecting an ON-sensor set to be turned on to provide services on which said target service instances are based, according to a critical covering set corresponding to the target service instances, the use history data of sensors in the critical covering set and energy parameters of said sensors; a controlling step for performing, when said time period begins, an ON/OFF control on the sensors in the IoT so that the sensors in the ON-sensor set are turned on and the sensors other than those in the ON-sensor set are turned off; and an updating step for updating the use history data of sensors according to the usage of sensors in said time period.

TECHNICAL FIELD

The present invention relates to a field of Internet of Things (IoT).More specifically, the present invention relates to a system and amethod for efficient service-instance oriented energy management in theIoT.

DESCRIPTION OF THE RELATED ART

The IoT is an important component of the new generation informationtechnology, and its full name is “The Internet of Things”. Thus, the IoTis the Internet over which things are connected with each other, as itsname implies. Specifically, the IoT is a huge network formed incombination with the Internet, in which through various informationsensor apparatuses such as sensor, Radio Frequency Identification (RFID)technology, Global Positioning System, infrared sensor, laser scanner,gas sensor or the like, any object or process requiring monitoring,connecting, interacting with is acquired in real time, and variousnecessary information such as its sound, light, heat, electricity,mechanics, chemistry, biology, position, etc. is acquired. The purposeof the IoT is to achieve connections between things and things, thingsand human beings, all things and networks, to facilitate recognition,management and control.

Most of the IoT access networks are wireless. In some cases, sensors foracquiring various information may even be deployed in the field. In thiscase, a sensor is powered by its battery, so its power capacity islimited, and these sensors seldom move. When a sensor runs out itspower, it may be not practical to replace battery of this sensormanually in the wild field (e.g., for a case where the sensor isdeployed in a sounding balloon, or on sea floor, or in volcanic crater).Moreover, in a case where sensors are very cheap, it is rathertroublesome to find sensors deployed in the wild field for replacingtheir batteries. So, how to keep the energy of the entire IoT network tomake it work for a longer time becomes a great challenge.

FIG. 2 is a diagram illustrating the deployment position of an IoTenergy management server which is adapted to implement the presentinvention, in the IoT. In the prior art, there is an applicationdeployed in the layer of access network for performing energy managementon sensors in a single domain. However, there are limitations onmanagement in this level. For example, taking a service that provides anaverage temperature of the Beijing city as an example, it is assumedthat the average temperature provided by the service is based on anaverage value of sensing values of a temperature sensor physicallylocated in a domain A in the north of the Beijing city and a temperaturesensor b physically located in a domain B in the south of the Beijingcity. When energy management is performed on sensors in accordance withthe level of access network, since energy management patterns may bedifferent between different domains and the management is usuallyirrelevant, the following case becomes possible: the power of thetemperature sensor a remains a lot, while the power of the temperaturesensor b is used out. So the temperature in the south of the Beijingcity can not be acquired, and it is impossible to continue providing theservice regarding the average temperature of the Beijing city, whichleads to a low Quality of Service (QoS).

The prior arts include, for example, IEEE 802.11 Power Save Mode (PSM).This technology reduces a device's idle listening time by periodicallyentering it into the sleep state, thereby causing the device to savepower.

In addition, in the well known Sensor-MAC (S-MAC) protocol, nodes in thenetwork periodically sleep, and neighbors form virtual clusters toauto-synchronize on sleep schedules.

All the above prior arts are designed for a specific network domain ornetwork protocol, and do not support flexible combination of providedservice capabilities.

SUMMARY OF THE INVENTION

Based on the above description, it is desirable to provide a method anda system capable of efficiently scheduling ON/OFF states of sensors inthe context of the IoT so as to save power to a considerable extent inthe long term while satisfying the QoS requirements.

In order to solve the above technical problems, the inventors of thepresent invention have proposed that energy management should beperformed in terms of service instances, but not directly in terms ofsensors. According to one aspect of the present invention, there isprovided a service-instance oriented energy management method in theIoT, comprising: predicting target service instances to be serviced in asubsequent time period, based on a service instance transition model;selecting an ON-sensor set to be turned on to provide services on whichsaid target service instances are based, according to a criticalcovering set corresponding to the target service instances, the usehistory data of sensors in the critical covering set and energyparameters of said sensors; and in response to start of said timeperiod, performing an ON/OFF control on the sensors in the IoT so thatthe sensors in the ON-sensor set are turned on and the sensors otherthan those in the ON-sensor set are turned off.

According to another aspect of the present invention, there is provideda service-instance oriented energy management system in the IoT,comprising: a prediction means configured to predict target serviceinstances to be serviced in a subsequent time period, based on a serviceinstance transition model; a selection means configured to select anON-sensor set to be turned on to provide services on which said targetservice instances are based, according to a critical covering setcorresponding to the target service instances, the use history data ofsensors in the critical covering set and energy parameters of saidsensors; and a control means configured to perform, in response to startof said time period, an ON/OFF control on the sensors in the IoT so thatthe sensors in the ON-sensor set are turned on and the sensors otherthan those in the ON-sensor set are turned off.

The method and the system of the present invention are not dependent onany MAC layer protocols, and they are transparent to lower layercommunication protocols, and are compatible with any MAC layerprotocols. The method and the system of the present invention supportflexible combination of provided service capabilities. In addition, themethod and the system of the present invention can consider dynamicchanges in running the application.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention itself and embodiments, other objects andadvantages thereof will be better understood by reading the followingdetailed description of illustrative embodiments with reference todrawings in which:

FIG. 1 illustrates a block diagram of an exemplary computing system 100which is adapted to implementing an embodiment of the present invention.

FIG. 2 is a diagram illustrating the deployment position of an IoTenergy management server which is adapted to implementing the presentinvention, in the IoT.

FIG. 3 illustrates the relationship between applications and serviceinstances by a simple example.

FIG. 4 illustrates a service instance state transition diagram based onthe example of FIG. 3.

FIG. 5 illustrates a service instance state transition diagram withtransition probabilities based on the example of FIG. 3.

FIG. 6 is a diagram exemplifying the deployment relationship betweensensors and service instances.

FIG. 7 is a flowchart illustrating a method for service-instanceoriented energy management in the IoT according to an embodiment of thepresent invention.

FIG. 8 is a diagram illustrating an example of the deploymentrelationship between sensors and service instances.

FIG. 9 is a diagram illustrating an example of the use history data ofsensors and the execution history of a service instance corresponding tothe example in FIG. 8.

FIG. 10 illustrates a sensor ON/OFF state transition diagram based onthe example of FIG. 9.

FIG. 11 illustrates a service instance state transition diagram based onthe example of FIG. 9.

FIG. 12 is a flowchart illustrating a method for service-instanceoriented energy management in the IoT according to another embodiment ofthe present invention.

FIG. 13 illustrates a system for service-instance oriented energymanagement in the IoT according to the present invention.

Now, preferred methods and systems are described with reference todrawings wherein the same reference numbers are used to indicate thesame elements in the drawings. In the following description, for anexplanatory purpose, many specific details are set forth in order tohelp fully understand systems and methods, etc. In other examples, inorder to simplify the description, commonly used structures and devicesare illustrated in a form of block diagram. Many modifications and otherembodiments may be conceived of by those skilled in the art, which ownthe benefits taught in the specification and drawings as well.Therefore, it should be understood that the present invention is notlimited to the disclosed specific embodiments, and additional possibleembodiments should be contained in the scope and exemplary inventiveconcept of the present invention. Although some specific terms are usedherein, they are simply used in a general descriptive sense but not fora limiting purpose.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

In the following discussion, a great amount of concrete details areprovided to help thoroughly understand the present invention. However,it is apparent to those of ordinary skill in the art that even thoughthere are no such concrete details, the understanding of the presentinvention would not be influenced. In addition, it should be furtherappreciated that any specific terms used below are only for theconvenience of description, and thus the present invention should not belimited to only use in any specific applications represented and/orimplied by such terms.

FIG. 1 shows an exemplary computer system 100 which is applicable toimplement the embodiments of the present invention. As shown in FIG. 1,the computer system 100 may include: CPU (Central Process Unit) 101, RAM(Random Access Memory) 102, ROM (Read Only Memory) 103, System Bus 104,Hard Drive Controller 105, Keyboard Controller 106, Serial InterfaceController 107, Parallel Interface Controller 108, Display Controller109, Hard Drive 110, Keyboard 111, Serial Peripheral Equipment 112,Parallel Peripheral Equipment 113 and Display 114. Among above devices,CPU 101, RAM 102, ROM 103, Hard Drive Controller 105, KeyboardController 106, Serial Interface Controller 107, Parallel InterfaceController 108 and Display Controller 109 are coupled to the System Bus104. Hard Drive 110 is coupled to Hard Drive Controller 105. Keyboard111 is coupled to Keyboard Controller 106. Serial Peripheral Equipment112 is coupled to Serial Interface Controller 107. Parallel PeripheralEquipment 113 is coupled to Parallel Interface Controller 108. And,Display 114 is coupled to Display Controller 109. It should beunderstood that the structure as shown in FIG. 1 is only for theexemplary purpose rather than any limitation to the present invention.In some cases, some devices may be added to or removed from the computersystem 100 based on specific situations.

In the following explanation, firstly, key terms and concepts used inthis disclosure are described.

Different sensors in the IoT provide different sensing capabilities. Forexample, a temperature sensor may sense the temperature, and a humiditysensor may sense the humidity, etc. In the present invention, thesesensing capabilities are packaged into many different services to beprovided to applications. A service in the present invention refers tothe basic service that can be provided to an application, such as“temperature in 3rd-Floor Room 301” and “humidity in 3rd-Floor Room301”. That is, the service in the present invention is the basicgranularity providing a specific IoT sensing service. An application isonly required to combine different services to implement a particularapplication scenario.

The method and the system according to the present invention areimplemented in the application infrastructure layer of the IoT (see FIG.2). For example, they are implemented in the IoT energy managementserver in FIG. 2. The IoT energy management server is used to controlthe duty cycle of a physical sensor to keep the whole system the mostenergy-efficient. The IoT application infrastructure is a distributedmiddleware platform to support the execution of IoT applications. Theapplication infrastructure platform provides the capabilities ofmanaging physical sensors as services. For example, both of “temperaturein 3rd-Floor Room 301” and “humidity in 3rd-Floor Room 301” are examplesof services achieved by the application infrastructure. With flexiblecombination of various services achieved by the applicationinfrastructure, IoT applications may form their application logicsuitable for various practical scenarios. Each service may require theinvolvement of physical sensors from different network domains, such asthe above example of the average temperature of the Beijing city.

For each service, its runtime instance is defined as a service instance(SI). A service instance is an instance of a service that can beprovided based on a combination of sensors. When an application actuallyuses (invokes) a service, the service instance of the service isgenerated and run in the IoT, and the running requires correspondingsensors to work. Energy management performed by the method and thesystem according to the present invention is based on service instancesinstead of sensors. In doing so, there are the following advantages:

1. For services, each service instance may be accomplished by adifferent combination of sensors, and particulars of sensor apparatusesmanagement are screened for applications, thereby enhancing flexibilityof the application development;

2. For sensor apparatuses, services provided by particular sensorapparatuses can be sufficiently considered in managing the sensorapparatuses, thereby ensuring reliability of services; therefore, theenergy management pattern based on service instances ensures flexibilityand reliability while minimizing the system energy consumption, and canbe conveniently transplanted to other IoT application support platforms.

FIG. 3 illustrates the relationship between applications and serviceinstances by a simple example. In FIG. 3, for example, service instance1 is detecting of the temperature in room 301, service instance 2 isdetecting of the humidity in room 301, and service instance 3 is thelighting in room 301. In FIG. 3, applications 1-3 use service instances1-3 respectively to form their own application logics. With reference tothe state transition in FIG. 3, for example, application 1 iterativelyexecutes the following application logic: detecting the temperature inroom 301, then detecting the humidity in room 301, and then adjustingthe lighting in room 301. Application 2, for example, iterativelyexecutes the following application logic: detecting the temperature inroom 301, then adjusting the lighting in room 301, and then detectingthe humidity in room 301. In application 3, the following serviceinstances are iteratively executed, respectively: detecting thetemperature in room 301, detecting the humidity in room 301 as well asadjusting the lighting in room 301. FIG. 3 is only an example given forsimplifying the explanation. In practice, an application may havecomplex application logic formed by many different service instances

As for the example in FIG. 3, when a plurality of IoT applications(applications 1-3) are executed simultaneously, there is one of thefollowing states within a certain period: one of service instances 1, 2,3 is executed alone, service instances (1, 2) are executed, serviceinstances (1, 3) are executed, service instances (2, 3) are executed,service instances (1, 2, 3) are executed.

FIG. 4 illustrates a service instance state transition diagram based onthe example of FIG. 3. In FIG. 4, there are 7 states, and it is possibleto transit from each state to one of the other six states. In a casewhere it is assumed that the probability that each of applications 1-3appears is ⅓ and the probability that a service instance j transits to aservice instance k of an application i (i=1˜3) is the same, the serviceinstance state transition diagram with transition probabilities in FIG.5 is obtained. The service instance state transition diagram withtransition probabilities is referred to as a service instance transitionmodel in the present invention. The service instance transition model isobtained through the offline learning to the execution history ofservice instances, which will be described in detail later. In addition,probabilities given in FIG. 5 are only exemplary and are not limitationson the present invention.

Below, a concept of critical covering set is introduced. A criticalcovering set corresponds to service instances, and it is a set ofvarious combinations of one or more sensors that have capabilities ofproviding services on which the service instances are based. FIG. 6 is adiagram exemplifying the deployment relationship between sensors andservice instances. In FIG. 6, 10 sensors (round dots) and 3 serviceinstances (square dots) are shown. These sensors may be located indifferent domains of the IoT. It is possible to automatically generatethe critical covering set in an offline state according to informationof sensors and information of service instances. For example,information of sensors may include, for example, coverages (ranges) ofsensors and coordinates of sensors; and information of service instancesmay include, for example, coordinates of service instances. Taking theservice instance 1 in FIG. 6 as an example, according to information ofsensors and information of service instances, it is possible toautomatically generate the critical covering setS₁={{1},{3},{5},{1,3},{1,5},{3,5},{1,3,5}{ of the service instance 1 inan offline state. Each combination (i.e., each element) in the criticalcovering set has a capability of providing services on which the serviceinstance is based. That is, turning on sensors 1, 3 or 5 alone, as wellas turning on sensors (1, 3), (1, 5), (3, 5) or (1, 3, 5) simultaneouslycan provide services on which the service instance 1 is based.

In addition to automatically generating critical covering sets,sometimes in consideration of other complex factors, it is also possibleto set said critical covering sets manually in advance. For example,regarding the example in FIG. 6, it is possible to set various criticalcovering sets in advance as follows.

S₁={{1}, {3,5}}

S₂={{2,4}, {2,5}, {4,5}}

S₃={{6}, {5,8}, {8,9}}

S_({1,2})=S1×S2

S_({2,3})=S2×S3

S_({1,3})=S1×S3

S_({1,2,3})=S1×S2×S3

where S₁ denotes the critical covering set of the service instance i,S_({1,2}) denotes the critical covering set that satisfies the conditionin which the service instances 1 and 2 are executed simultaneously andis equal to S₁+S₂, the operator “×” here denotes the Cartesian productof S₁ and S₂. For example, here, S_({1,2})={{1,2,4}, {1,2,5}, {1,4,5},{2,3,4,5}, {2,3,5}, {3,4,5}}. Furthermore, definitions of S_({2,3}),S_({1,3}) and S_({1,2,3}) are similar to that of S_({1,2}).

FIG. 7 is a flowchart illustrating a method for service-instanceoriented energy management in the IoT according to an embodiment of thepresent invention. The energy management method is executed in the IoTenergy management server in the IoT application infrastructure. The IoTenergy management server is used to perform centralized energymanagement on the whole IoT. The processing in the flowchart in FIG. 7includes the following steps:

Step 710: predicting step.

Step 720: selecting step.

Step 730: controlling step.

In the following, each step in the energy management method of thepresent invention in FIG. 7 is described in detail.

Step 710: Predicting Step

In step 710, a target service instance to be serviced in a subsequenttime period is predicted based on the service instance transition model.

In the following, an explanation of each step in the energy managementmethod of the present invention is made in combination with a specificsimple example. FIG. 8 is a diagram illustrating an example of thedeployment relationship between sensors and service instances. In FIG.8, there are two sensors and one service instance, i.e., sensor 1,sensor 2 and service instance 1. According to the topology in FIG. 8,each of the two sensors can satisfy the execution of the serviceinstance 1 individually.

The service instance transition model in step 710 is obtained throughthe offline learning to the execution history of service instances. FIG.9 is a diagram illustrating an example of the use history data ofsensors and the execution history of the service instance correspondingto the example in FIG. 8. For example, the energy management method ofthe present invention is executed at the decision point in FIG. 9. Inthe lower part in FIG. 9, the execution history of the service instance1 is shown. According to the execution history of the service instance 1in FIG. 9, it is possible to obtain the following statistics of thestate transition of the service instance 1: it is assumed that there are45 time units between the origin and the decision point, wherein in 33time units the service instance is in continuous execution (i.e., theprevious time unit is for execution and the subsequent time unit is alsofor execution), in 4 time units it is from execution to disappearance,in 4 time units it is from disappearance to execution, and in 4 timeunits it is in continuous disappearance.

According to the above statistics of the execution history of theservice instance 1, it is possible to conclude that in the next timeperiod (a predetermined time period starting from the decision point),the probability that the service instance 1 continues to be executed is33/(33+4)=0.89; the probability that the service instance 1 ceases is4/(33+4)=0.11; the probability of transiting from a non-service instancestate to the execution of the service instance 1 is 4/(4+4)=0.5; theprobability of maintaining the non-service instance state is4/(4+4)=0.5. Thus, the state transition diagram shown in FIG. 11 isobtained.

FIG. 11 illustrates a service instance state transition diagram based onthe example of FIG. 9. The service instance state transition diagramwith transition probabilities corresponds to the service instancetransition model in step 710. According to the service instancetransition model in FIG. 11, given that the service instance 1 iscurrently serviced (see FIG. 9), in step 710, the next service instanceto be serviced is predicted. A random selection policy is adopted in thepresent invention. Here, a random number rand (rand is a random numberin the range of [0, 1]) is generated and compared with the probabilityof transiting outwardly from the service instance 1, and we have:

If rand≦0.89, →service instance 1;

If rand>0.89, →none

where “none” denotes a state in which no service instance is serviced.Thus, according to the generated random number, a service instance to beserviced in the next time period (cycle) is predicted. Given thatrand=0.3, in step 710, the service instance 1 is predicted to beserviced in the next time period.

In the above, a simplest example is given only for facilitating theexplanation. In fact, a plurality of service instances will be executedsimultaneously in the IoT. According to the execution history of theseservice instances, service instance state transition diagrams similar tothose in FIGS. 5 and 11 may be also obtained. The more the serviceinstances are, the more complex the transition diagrams become.

The inventors of the present invention have proposed that energymanagement should be performed in a service-instance oriented way butnot directly in a sensor-oriented way. Here, the reason is given withreference to FIGS. 8 and 9. Here, the ON/OFF states of sensors in FIG. 8are defined as: ON:1; OFF:0, so that four kinds of ON/OFF states (sensor2, sensor 1) of sensors are obtained: (0, 1), (1, 0), (1, 1), (0, 0). Itis assumed that the use history data of sensors is as shown in the upperpart in FIG. 9. Likewise, there are 45 time units between the origin andthe decision point. And it is assumed that the measures from the drawingare: the sensor 1 is ON for a total time of 32 time units and is OFF fora total time of 13 time units; and the sensor 2 is ON for a total timeof 20 time units and is OFF for a total time of 25 time units.

According to FIG. 9, the sensor ON/OFF state transition frequencies andtransition probabilities are obtained as follows:

(0,0)→(0,1): once=>0.5 (1,0)→(0,1): 0 times=>0

(0,0)→(1,0): once=>0.5 (1,0)→(0,0): 0 times=>0

(0,0)→(1,1): 0 times=>0 (1,0)41,1): twice=>1

(1,1)→(0,1): twice=>0.67 (0,1)→(1,1): once=>0.33

(1,1)→(1,0): once=>0.33 (0,1)→(1,0): 0 times=>0

wherein the above transition probabilities are calculated based on thenumber of times of corresponding state transition. For example, statetransition originating from (0, 0) happens twice, once it is transitedto (0, 1) and once it is transited to (1, 0). So the correspondingtransition probabilities are 0.5, respectively.

In this way, the sensor ON/OFF state transition diagram shown in FIG. 10is obtained. As seen from FIG. 10, in the current state (0, 1), at thedecision point it is most possible to transit to the state (0, 0); andin the state (0, 0), both sensors 1 and 2 will be turned off. However,on the other hand, as seen from FIG. 9, in fact, the service instance 1is stilled executed after the decision point. Therefore, the sensorON/OFF control performed according to the sensor ON/OFF state transitiondiagram will not satisfy the execution of the service instance 1 in thefuture, and thus the quality of service is not satisfied.

In addition, the complexity of the method based on the sensor ON/OFFstate transition diagram is too high. Given that the network has Nsensors and M service instances in total, the method based on the sensorON/OFF state transition diagram has to record 2^(N) different states,each calculated state needs N bits for recording. Thereby, a total ofN2^(N) bits have to be stored. In the context of the IoT, the number Nof sensors is generally in the order of millions, and this number is farbeyond the storage capability of a general server, which is impracticalin implementation. For example, when N=1000, N2^(N)=1.3e³⁰³ bytes.

However, in the method based on the service instance transition model ofthe present invention, only 2^(M) different states have to be recorded,and M2^(M)bits are to be stored. In a scenario of the IoT, the number ofservice instances is generally small (e.g. M=10, this is because, forexample, although a kind of sensor is deployed in a factory in plenty,they execute the same task). Therefore, the calculation and storagecomplexity in this method is much lower than that in the method based onthe sensor ON/OFF state transition diagram. For example, when M=20,M2^(M)=2.6 megabytes.

In addition, it is note that the energy management method of the presentinvention is executed in a real-time way. In one embodiment, the serviceinstance transition model (e.g., in FIG. 11) is obtained through theoffline learning to the execution history of service instances. Saidlearning is performed periodically so that said service instancetransition model is updated periodically. For example, whenever thesystem or method of the present invention is ready to be online or at apredetermined timing or interval, on the basis of the latest executionhistory of service instances, a current service instance transitionmodel is obtained through learning. Then, when executed, the system ormethod of the present invention uses the service instance transitionmodel to make said prediction. In doing so, dynamic changes in theapplication running may be considered. For example, a service instanceused by an application running in the nighttime may be quite differentfrom a service instance used by an application running in the daytime.

Step 720: Selecting Step

In step 720, according to a critical covering set corresponding totarget service instances, the use history data of sensors in thecritical covering set and energy parameters of said sensors, anON-sensor set to be turned on to provide services on which said targetservice instances are based is selected.

Still, the case in FIGS. 8 and 9 is taken as an example for explanation.As described above, it is assumed that according to the above algorithm,in step 710 the service instance 1 is predicted to be serviced in thenext time period. The critical covering set corresponding to the serviceinstance 1 is S_({1})={{1},{2},{1,2}}. That is, sensors 1 and 2 cansatisfy the service instance 1 separately or in combination with eachother. The critical covering set may be automatically generated, or maybe preset.

Now, in step 720, it is necessary to select which sensor or sensors isused to satisfy the service instance 1.

The use history data of sensors (sensors 1 and 2) in the criticalcovering set S_({1}) is as described above: the sensor 1 is ON for atotal time of 32 time units and is OFF for a total time of 13 timeunits; and the sensor 2 is ON for a total time of 20 time units and isOFF for a total time of 25 time units. For example, energy parameters ofa sensor include at least initial energy of the sensor, energy consumedper unit time of the sensor and energy consumed by a single ON/OFFoperation of the sensor. In the example of FIGS. 8 and 9, it is assumedthat there are the following sensor energy parameters:

-   -   only the sensor 1 is ON: 0.1 energy unit (E)/time unit is        consumed    -   only the sensor 2 is ON: 0.1 energy unit/time unit is consumed    -   the initial energy of each sensor is 10E, each ON/OFF operation        of the sensor consumes 0.1E energy    -   when both sensors 1 and 2 are ON, each of them consumes 0.1        energy unit/unit time.

According to the upper part in FIG. 9, the statistics is obtained: up tothe decision point, the ON/OFF operation of the sensor 1 is performedfor 6 times, this consumes 0.6E energy, and the sensor 1 is ON for 32time units, this consumes 3.2E energy, thus 10E-0.6E-3.2E=6.2E energyremains; the ON/OFF operation of the sensor 2 is performed for 4 times,this consumes 0.4E energy, and the sensor 2 is ON for 20 time units,this consumes 2.0E energy, thus 10E-0.4E-2.0E=7.6E energy remains.

In step 720, in one embodiment, said selection is based on an energyconsumption rate, which is calculated according to a critical coveringset corresponding to target service instances, the use history data ofsensors in the critical covering set and energy parameters of saidsensors, in the cases where each combination in the critical coveringset is selected.

In the present invention, the energy consumption rate of each sensor isdefined as:

$r_{i}^{t} = {{\gamma_{1}\frac{t_{i}^{ON}}{T}} + {\gamma_{2}\frac{e_{i}}{E_{i}}}}$

where r_(i) ^(i) tis an energy consumption rate of the i-th sensor,t_(i) ^(ON) is a total ON time of the i-th sensor, T is a totalstatistical time period, e_(i) is energy consumed by a single ON/OFFoperation of the i-th sensor, E_(i) is the remaining energy of the i-thsensor, γ₁ and γ₂ are set weight coefficients.

The energy consumption rate in case where each combination in thecritical covering set is selected is:

$r_{s}^{t} = {\frac{1}{N}{\sum r_{i}^{t}}}$

where S denotes the critical covering set.

In one embodiment, for example, parameters are set as follows: γ_(1=0.2)and γ_(2=0.8). In the following, the energy consumption rate in a casewhere each element in the critical covering set is selected iscalculated.

If, in the next time period, only the sensor 1 provides a service(corresponding to {1} in S_({1})), the energy consumption rate of thesensor 1 is calculated as:

$\quad\left\{ {\left. \begin{matrix}{r_{1}^{t} = {{{0.2\frac{32\; T}{45\; T}} + {0.8\frac{0}{6.2\; E}}} = 0.142}} \\{r_{2}^{t} = 0}\end{matrix}\Rightarrow r_{S_{\{ 1\}}}^{t} \right. = {r_{1}^{t} = 0.142}} \right.$

wherein the current state is (0, 1), the sensor 1 is in the ON state, soit is not necessary to perform the ON/OFF operation of the sensor,thereby e_(i=0); and T is the time unit. In addition, r_(s) _({1}) ^(t)denotes the energy consumption rate of S_({1}).

If, in the next time period, only the sensor 2 provides a service(corresponding to {2} in S_({1})), the energy consumption rate of thesensor 2 is calculated as:

$\quad\left\{ {\left. \begin{matrix}{r_{1}^{t} = 0} \\{r_{2}^{t} = {{{0.2\frac{20\; T}{45\; T}} + {0.8\frac{0.1\; E}{7.6\; E}}} = 0.0994}}\end{matrix}\Rightarrow r_{S_{\{ 1\}}}^{t} \right. = {r_{2}^{t} = 0.0994}} \right.$

If, in the next time period, the sensors 1 and 2 provide services incombination with each other (corresponding to {1, 2} in S_({1})), theenergy consumption rate of the sensors 1 and 2 is calculated as:

$\quad\left\{ {\left. \begin{matrix}{r_{1}^{t} = {{{0.2\frac{32\; T}{45\; T}} + {0.8\frac{0}{6.2\; E}}} = 0.142}} \\{r_{2}^{t} = {{{0.2\frac{20\; T}{45\; T}} + {0.8\frac{0.1\; E}{7.6\; E}}} = 0.0994}}\end{matrix}\Rightarrow r_{S_{\{ 1\}}}^{t} \right. = {{\frac{1}{2}\left( {r_{1}^{t} + r_{2}^{t}} \right)} = 0.121}} \right.$

Because based on the prediction in step 710, in order to satisfy theservice instance 1, it is not possible for the sensors 1 and 2 to be OFFsimultaneously, a case where the sensors 1 and 2 are OFF simultaneouslyis not considered.

Then, based on the energy consumption rate, an ON-sensor set to beturned on to provide services on which said target service instances arebased is selected. Specifically, in the above example, three calculatedenergy consumption rates are compared and the sensor set correspondingto the minimum energy consumption rate is selected.

$\left. 0.0994\leftarrow{\min \left\{ \begin{matrix}0.142 \\0.0994 \\0.121\end{matrix} \right.} \right.$

In the current example, the element {2} in the critical covering setS_({1}) is selected, i.e., the ON-sensor set is {2}. That is, in thenext time period, the sensor 2 is selected to be turned on.

On the other hand, if it is assumed that no service instance ispredicted to be serviced in the next time period in step 710, all thesensors are simply turned off in step 720.

The above-mentioned energy consumption rate is only an example given inthe present invention. Those skilled in the art may think of othermetrics used to compare various combinations of sensors (for example,energy consumed by the ON/OFF operation is not considered) so thatselection of combinations of sensors to be turned on may be done aswell.

Step 730: Controlling Step

In step 730, when the next time period begins, an ON/OFF control isperformed on the sensors in the IoT, the sensors in the ON-sensor setare turned on and the sensors other than those in the ON-sensor set areturned off.

For example, according to the above example, in step 730, when the nexttime period begins (i.e., at the decision point in FIG. 9), the sensor 2in the ON-sensor set {2} is turned on, and the sensor other than that inthe sensor set {2}, i.e., the sensor 1, is turned off.

FIG. 12 is a flowchart illustrating a method for service-instanceoriented energy management in the IoT according to another embodiment ofthe present invention. The processing in the flowchart in FIG. 12includes the following steps: a predicting step 1210, a selecting step1220, a controlling step 1230 and an updating step 1240, wherein theprocessing in steps 1210 to 1230 is similar to that in steps 710 to 730in FIG. 7.

In the updating step 1240, according to the usage of sensors in saidnext time period, the use history data of sensors is updated. That is,in a manner described in the upper part in FIG. 9, the accumulation ofthe use history data of sensors 1 and 2 in said next time period iscontinued.

After step 1240, the processing returns to step 1220, and steps 1220 to1240 are performed periodically. From the second execution, the usehistory data of sensors used in step 1220 during each execution is theuse history data of sensors updated in step 1240 during the lastexecution.

In the above, the inventive concept of the present invention isexplained by a simple example. However, the present invention is notlimited to the disclosed particular embodiments or particular values ofparameters. For example, the above energy consumption rate may be givenby other formulae, γ₁ and γ₂ may also be set to, for example, 0.3 and0.7, etc.

FIG. 13 illustrates a system 1300 for service-instance oriented energymanagement in the IoT according to the present invention. The energymanagement system 1300 includes a prediction means 1310, a selectionmeans 1320 and a control means 1330. The prediction means 1310 predictstarget service instances to be serviced in a subsequent time periodbased on the service instance transition model. The selection means 1320selects an ON-sensor set to be turned on to provide services on whichsaid target service instances are based, according to a criticalcovering set corresponding to the target service instances, the usehistory data of sensors in the critical covering set and energyparameters of said sensors. The control means 1330 performs, when saidnext time period begins, an ON/OFF control on the sensors in the IoT,the sensors in the ON-sensor set are turned on and the sensors otherthan those in the ON-sensor set are turned off.

In another embodiment, the energy management system 1300 may furthercomprise an updating device 1340. The updating device 1340 updates theuse history data of sensors according to the usage of sensors in saidtime period.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

1. A service-instance oriented energy management method in the Internetof Things (IoT), comprising: predicting target service instances to beserviced in a subsequent time period based on a service instancetransition model; selecting an ON-sensor set to be turned on to provideservices on which said target service instances are based, whereinselecting the ON-sensor set to be turned on is based on a criticalcovering set corresponding to the target service instances, the usehistory data of sensors in the critical covering set, and energyparameters of said sensors; and performing an ON/OFF control on thesensors in the IoT in response to the start of said time period so thatthe sensors in the ON-sensor set are turned on and the sensors otherthan those in the ON-sensor set are turned off.
 2. The energy managementmethod according to claim 1, wherein said service instance is aninstance of service that is provided based on a combination of sensors.3. The energy management method according to claim 2, wherein thecritical covering set corresponding to service instances is a set ofvarious combinations of one or more sensors that are capable ofproviding services on which the service instances are based.
 4. Theenergy management method according to claim 1, further comprisingupdating the use history data of sensors according to the usage ofsensors in said time period, wherein said selecting, said controlling,and said updating are performed periodically, and from the secondexecution, the use history data of sensors used in said selecting duringeach execution is the use history data of sensors updated in saidupdating during the last execution.
 5. The energy management methodaccording to claim 1, wherein the energy parameters of said sensorsinclude at least initial energy of the sensors, energy consumed per unittime of the sensors and energy consumed by a single ON/OFF operation ofthe sensors.
 6. The energy management method according to claim 1,wherein said selection is based on an energy consumption rate, whereinthe energy consumption rate is calculated when each combination in thecritical covering set is selected based on the critical covering setcorresponding to the target service instances, the use history data ofsensors in the critical covering set, and energy parameters of saidsensors.
 7. The energy management method according to claim 1, whereinsaid critical covering set is preset.
 8. The energy management methodaccording to claim 1, wherein said critical covering set isautomatically generated in an offline state according to information ofsensors and information of service instances.
 9. The energy managementmethod according to claim 1, wherein said sensors are located indifferent domains of the IoT.
 10. The energy management method accordingto claim 1, wherein said service instance transition model is obtainedthrough the offline learning to the execution history of serviceinstances.
 11. The energy management method according to claim 10,wherein said learning is performed periodically so that said serviceinstance transition model is updated periodically.
 12. Aservice-instance oriented energy management system in the IoT,comprising: a prediction means for predicting target service instancesto be serviced in a subsequent time period based on a service instancetransition model; a selection means for selecting an ON-sensor set to beturned on to provide services on which said target service instances arebased, wherein selecting an ON-sensor set to be turned is based on acritical covering set corresponding to the target service instances, theuse history data of sensors in the critical covering set and energyparameters of said sensors; and a control means for performing an ON/OFFcontrol on the sensors in the IoT in response to start of said timeperiod, so that the sensors in the ON-sensor set are turned on and thesensors other than those in the ON-sensor set are turned off.
 13. Theenergy management system according to claim 12, wherein said serviceinstance is an instance of service that is provided based on acombination of sensors.
 14. The energy management system according toclaim 13, wherein the critical covering set corresponding to serviceinstances is a set of various combinations of one or more sensors thatare capable of providing services on which the service instances arebased.
 15. The energy management system according to claim 12, whereinthe energy parameters of said sensors include at least initial energy ofthe sensors, energy consumed per unit time of the sensors and energyconsumed by a single ON/OFF operation of the sensors.
 16. The energymanagement system according to claim 12, wherein the selecting is basedon an energy consumption rate, wherein the energy consumption rate iscalculated when each combination in the critical covering set isselected according to the critical covering set corresponding to thetarget service instances, the use history data of sensors in thecritical covering set and energy parameters of said sensors.
 17. Theenergy management system according to claim 12, wherein said criticalcovering set is preset.
 18. The energy management system according toclaim 12, wherein said critical covering set is automatically generatedin an offline state according to information of sensors and informationof service instances. 19.-22. (canceled)
 22. The energy managementsystem according to claim 12, further comprising an updating meansconfigured to update the use history data of sensors according to theusage of sensors in said time period.
 23. A non-transitory computerreadable storage medium tangibly embodying a computer readable programcode having computer readable instructions which, when implemented,cause a computer device to carry out the steps of a method comprising:predicting target service instances to be serviced in a subsequent timeperiod based on a service instance transition model; selecting anON-sensor set to be turned on to provide services on which said targetservice instances are based, wherein selecting the ON-sensor set to beturned on is based on a critical covering set corresponding to thetarget service instances, the use history data of sensors in thecritical covering set, and energy parameters of said sensors; andperforming an ON/OFF control on the sensors in the IoT in response tothe start of said time period so that he sensors in the ON-sensor setare turned on and the sensors other than those in the ON-sensor set areturned off.